Leverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio. Purchase of the print or Kindle book includes a free eBook in the PDF format. Key Features Design, train, and evaluate machine learning algorithms that underpin automated trading strategies Create a research and strategy development process to apply predictive modeling to trading decisions Leverage NLP and deep learning to extract tradeable signals from market and alternative data Book DescriptionThe explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models. This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research. This edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples. By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance. What you will learn Leverage market, fundamental, and alternative text and image data Research and evaluate alpha factors using statistics, Alphalens, and SHAP values Implement machine learning techniques to solve investment and trading problems Backtest and evaluate trading strategies based on machine learning using Zipline and Backtrader Optimize portfolio risk and performance analysis using pandas, NumPy, and pyfolio Create a pairs trading strategy based on cointegration for US equities and ETFs Train a gradient boosting model to predict intraday returns using AlgoSeek's high-quality trades and quotes data Who this book is forIf you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies. Some understanding of Python and machine learning techniques is required.

Explore effective trading strategies in real-world markets using NumPy, spaCy, pandas, scikit-learn, and Keras Key Features Implement machine learning algorithms to build, train, and validate algorithmic models Create your own algorithmic design process to apply probabilistic machine learning approaches to trading decisions Develop neural networks for algorithmic trading to perform time series forecasting and smart analytics Book DescriptionThe explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This book enables you to use a broad range of supervised and unsupervised algorithms to extract signals from a wide variety of data sources and create powerful investment strategies. This book shows how to access market, fundamental, and alternative data via API or web scraping and offers a framework to evaluate alternative data. You'll practice the ML workflow from model design, loss metric definition, and parameter tuning to performance evaluation in a time series context. You will understand ML algorithms such as Bayesian and ensemble methods and manifold learning, and will know how to train and tune these models using pandas, statsmodels, sklearn, PyMC3, xgboost, lightgbm, and catboost. This book also teaches you how to extract features from text data using spaCy, classify news and assign sentiment scores, and to use gensim to model topics and learn word embeddings from financial reports. You will also build and evaluate neural networks, including RNNs and CNNs, using Keras and PyTorch to exploit unstructured data for sophisticated strategies. Finally, you will apply transfer learning to satellite images to predict economic activity and use reinforcement learning to build agents that learn to trade in the OpenAI Gym. What you will learn Implement machine learning techniques to solve investment and trading problems Leverage market, fundamental, and alternative data to research alpha factors Design and fine-tune supervised, unsupervised, and reinforcement learning models Optimize portfolio risk and performance using pandas, NumPy, and scikit-learn Integrate machine learning models into a live trading strategy on Quantopian Evaluate strategies using reliable backtesting methodologies for time series Design and evaluate deep neural networks using Keras, PyTorch, and TensorFlow Work with reinforcement learning for trading strategies in the OpenAI Gym Who this book is forHands-On Machine Learning for Algorithmic Trading is for data analysts, data scientists, and Python developers, as well as investment analysts and portfolio managers working within the finance and investment industry. If you want to perform efficient algorithmic trading by developing smart investigating strategies using machine learning algorithms, this is the book for you. Some understanding of Python and machine learning techniques is mandatory.

Diplomarbeit aus dem Jahr 2002 im Fachbereich Jura - Offentliches Recht / Sonstiges, Note: 15/15, Fachhochschule des Bundes fur offentliche Verwaltung Bruhl - Fachbereich Allgemeine Innere Verwaltung, Sprache: Deutsch, Abstract: Auszuge aus der Einleitung: Die Landesregierung Schleswig-Holsteins hat im Jahr 1990 den Versuch unternommen, im Bereich der Mitbestimmung des Offentlichen Dienstes neue Wege zu gehen. Mit Verabschiedung des Mitbestimmungsgesetz Schleswig-Holstein alter Fassung (MBG Schl.-H. a.F.) am 27.11.1990 wurde dem Gesetz nicht nur eine neue Bezeichnung gegeben sondern im Schwerpunkt dem Personalrat weitergehende Beteiligungsrechte verliehen. Es handelt sich dabei um eine Konzeption, die effektive und paritatische Beteiligung von Personalvertretungen ermoglichen soll. Gepragt von sog. Allzustandigkeit wird das Ziel verfolgt, bei allen Massnahmen ein nahezu gleichberechtigtes Miteinander zwischen Dienststelle und Personalrat, unter Berucksichtung des gesellschaftlichen, wirtschaftlichen und okologischen Umfeldes zu ermoglichen. In der politischen Landschaft war dieses Gesetz nicht unumstritten und so haben 282 Abgeordnete des Deutschen Bundestages von CDU/FDP einen Normenkontrollantrag beim BVerfG gestellt. Der Entscheid des 2. Senats des BVerfG zum MBG Schl.-H. a.F. wurde in allen Kreisen mit Spannung erwartet. Insbesondere inwieweit dem Modernisierungsgedanken Schleswig-Holsteins Rechnung getragen wird und ob ein gangbarer Weg besteht, Formen der Betriebsverfassung in den Offentlichen Dienst zu integrieren. So war die Verwunderung besonders bei denen gross, die sich fur Reformen, Modernisierungen und infolgedessen fur das MBG Schl.-H. a.F. ausgesprochen haben. Denn der 2. Senat hat das Gesetz in wesentlichen Teilen als verfassungswidrig eingestuft. Der Entscheid wurde in vielfach trefflicher Weise kommentiert und kritisiert; darum soll es in dieser Arbeit nicht gehen. Vielmehr hat mich der Titel Grenzen der Mitbestimmung im Offentlichen Dienst"